Chemokines Up-Regulated in Epithelial Cells Control Senescence-Associated T Cell Accumulation in Salivary Glands of Aged and Sjögren’s Syndrome Model Mice

Immunosenescence is characterized by age-associated changes in immunological functions. Although age- and autoimmune-related sialadenitis cause dry mouth (xerostomia), the roles of immunosenescence and cellular senescence in the pathogenesis of sialadenitis remain unknown. We demonstrated that acquired immune cells rather than innate immune cells infiltrated the salivary glands (SG) of aged mice. An analysis of isolated epithelial cells from SG revealed that the expression levels of the chemokine CXCL13 were elevated in aged mice. Senescence-associated T cells (SA-Ts), which secrete large amounts of atypical pro-inflammatory cytokines, are involved in the pathogenesis of metabolic disorders and autoimmune diseases. The present results showed that SA-Ts and B cells, which express the CXCL13 receptor CXCR5, accumulated in the SG of aged mice, particularly females. CD4⁺ T cells derived from aged mice exhibited stronger in vitro migratory activity toward CXCL13 than those from young mice. In a mouse model of Sjögren’s syndrome (SS), SA-Ts also accumulated in SG, presumably via CXCL12-CXCR4 signaling. Collectively, the present results indicate that SA-Ts accumulate in SG, contribute to the pathogenesis of age- and SS-related sialadenitis by up-regulating chemokines in epithelial cells, and have potential as therapeutic targets for the treatment of xerostomia caused by these types of sialadenitis.     

Maximum Data Generation Rate Routing Protocol Based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

For rechargeable wireless sensor networks, limited energy storage capacity, dynamic energy supply, low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario. Therefore, before data delivery, a sensor has to update its waking schedule continuously and share them to its neighbors, which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets. In this work, we propose the maximum data generation rate routing protocol based on data flow controlling technology. For a sensor, it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors. Hence, the energy consumption for time synchronization, location information and waking schedule shared will be reduced significantly. The saving energy can be used for improving data collection rate. Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.     

一种基于小字典不对等语料的跨语言词嵌入方法

双语词嵌入通常采用从源语言空间到目标语言空间映射,通过源语言映射嵌入到目标语言空间的最小距离线性变换实现跨语言词嵌入。然而大型的平行语料难以获得,词嵌入的准确率难以提高。针对语料数量不对等、双语语料稀缺情况下的跨语言词嵌入问题,该文提出一种基于小字典不对等语料的跨语言词嵌入方法,首先对单语词向量进行归一化,对小字典词对正交最优线性变换求得梯度下降初始值,然后通过对大型源语言(英语)语料进行聚类,借助小字典找到与每一聚类簇相对应的源语言词,取聚类得到的每一簇词向量均值和源语言与目标语言对应的词向量均值,建立新的双语词向量对应关系,将新建立的双语词向量扩展到小字典中,使得小字典得以泛化和扩展。最后,利用泛化扩展后的字典对跨语言词嵌入映射模型进行梯度下降求得最优值。在英语—意大利语、德语和芬兰语上进行了实验验证,实验结果证明该文方法可以在跨语言词嵌入中减少梯度下降迭代次数,减少训练时间,同时在跨语言词嵌入上表现出较好的正确率。     

User activities and the heterogeneity of urban space:The case of Dahiyat Al Hussein park

Urban researchers have maintained a constant interest in the complexity and continuity of urban space usage. Some have applied actor–network theory (ANT) to investigate the heterogeneity of spaces and present them through the networks of their users’ activities. However, these accounts are predominantly limited in examining the extent to which these spaces may be heterogeneous when exploring such networks. This paper draws on recent ANT scholarship, which employs an ethnographic research conducted in a main park in a housing project at Dahiyat Al Hussein in Amman, Jordan. The findings describe the complex and unpredictable negotiations that occur within spaces by documenting the varieties and interrelations among user activity networks within this common and shared urban space. This research reveals the extent to which spaces, parks in this case, may be heterogeneous by unpacking their usage. The conclusions and insights assert the necessity of paying attention to design detail and creating designs that are responsive to evolving user activities.     

Modeling and analysis of passive worm propagation in the P2P file-sharing network

A number of worms, named P2P (peer-to-peer) passive worms, have recently surfaced, which propagate in P2P file-sharing networks and have posed heavy threats to these networks. In contrast to the majority of Internet worms, it is by exploiting users’ legitimate activities instead of vulnerabilities of networks in which P2P passive worms propagate. This feature evidently slows down their propagation, which results in them not attracting an adequate amount of attention in literature. Meanwhile, this feature visibly increases the difficulty of detecting them, which makes it very possible for them to become epidemic. In this paper, we propose an analytical model for P2P passive worm propagation by adopting epidemiological approaches so as to identify their behaviors and predict the tendency of their propagation accurately. Compared with a few existing models, dynamic characteristics of P2P networks are taken into account. Based on this proposed model, the sufficient condition for the global stability of the worm free equilibrium is derived by applying epidemiological theories. Large scale simulation experiments have validated both the proposed model and the condition.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.     

Optimal scheduling of single stage batch plants with direct heat integration

This paper addresses the multi-objective optimization problem arising in the operation of heat integrated batch plants, where makespan and utility consumption are the two conflicting objectives. A new continuous-time MILP formulation with general precedence variables is proposed to simultaneously handle decisions related to timing, product sequencing, heat exchanger matches (selected from a two-stage superstructure) and their heat loads. It features a complex set of timing constraints to synchronize heating and cooling tasks, derived from Generalized Disjunctive Programming. Through the solution of an industrial case study from a vegetable oil refinery, we show that major savings in utilities can be achieved while generating the set of Pareto optimal solutions through the ɛ-constraint method.     

积极降压对脑小血管病防治策略的影响

2017年,美国心脏病学会和美国心脏协会提出将高血压诊断标准降为130/80 mm Hg (1 mm Hg=0. 133 kPa),这与其他指南有所不同。降压治疗的核心目标在于靶器官的保护,近年来降压治疗与脑小血管病预防的相关问题日益受到临床重视。目前研究结果表明,降压治疗对腔隙性卒中患者的二级预防以及白质病变进展的预防可能有一定积极意义,但确切的降压目标值并未确定。血压与临床结局可能呈现J型关系,血压过低或过高可能均有害,而取得最大获益的降压目标有待进一步探索。